Author: Ye Hong

We are pleased to share that Dr. Ye Hong, a postdoctoral researcher at MIE Lab, will join the Department of Human Geography at Lund University as Assistant Professor in (Geo)AI and Regional Development starting in May 2026.

Ye’s research focuses on GIS, human mobility analysis, and generative (Geo)AI. His work develops data-driven methods to understand how people move and interact with urban environments, with applications in sustainable transportation, urban planning, and regional development.

At Lund University, Ye will continue advancing research on mobility data and artificial intelligence for spatial analysis, contributing to interdisciplinary work on sustainable urban systems and regional development.

We congratulate Ye on this appointment and wish him all the best as he begins this new chapter in Sweden.

Victoria is a visiting PhD student at the MIE Lab in fall 2025 and spring 2026. She has been pursuing her PhD at the Chair of Traffic Engineering and Control at the Technical University of Munich since 2022, and has a background in Computational Science and Engineering and Civil Engineering. Her research interests revolve around leveraging geospatial data to address transportation challenges. This includes the use of trajectory data for bicycle routing and mode choice modelling, where she employs optimisation and machine learning techniques to develop data-driven solutions.

Methodological focus: Spatio-temporal data mining and analysis, machine learning, and predictive models.

Martin Raubal gave the keynote speech at the ZVR Traffic Law Conference 2025 at the Vienna University of Economics and Business in Austria. His talk ‘Sustainable Mobility – quo vadis’ focused on the importance of geodata and -technologies for sustainable mobility and introduced current research on electric mobility, V2G technologies, and behavior change. The slides of the talk (in German) are available here.

Ye Hong gave a talk at the Swiss Transport Research Conference (STRC) 2024 with the title Towards realistic individual activity location demand synthesis using deep generative networks. Reach out if you are interested in the topic and want to learn more!

Our new paper entitled “Evaluating geospatial context information for travel mode detection” was accepted at Journal of Transport Geography and is now available (open-access!) online.

How much does geospatial context information contribute to travel mode detection?

Our latest study reveals that geospatial network features, such as distance to the road network, are more critical than motion features, such as speed and acceleration, when classifying an extensive list of travel modes. Still, most land-use and land-cover features barely contribute to the task. The results are based on our extensive context representation reviews and the proposed analytical pipeline to assess the contribution of geospatial context information based on a random forest model and the SHapley Additive exPlanation (SHAP) method.

The study provides valuable guidance for feature selection, effective feature design, and building efficient travel mode detection models.

Check out the paper online and the corresponding code on Github!

Our new paper entitled “Context-aware multi-head self-attentional neural network model for next location prediction” was accepted at Transportation Research Part C: Emerging Technologies and is now available (open-access!) online.

We present a multi-head self-attentional (MHSA) neural network that integrates location features, temporal features, and functional land use contexts for next location prediction. This comprehensive approach effectively captures movement-related spatio-temporal information, leading to state-of-the-art performance on GNSS mobility datasets.

Our analysis demonstrates that training the model on population data yields superior results by learning from collective movement patterns, surpassing the capabilities of individual-level models. Moreover, we emphasize the significance of recent past movements and weekly periodicity, showing that learning from a subset of historical mobility is sufficient to obtain an accurate location prediction result.

The proposed model represents a pivotal advancement in accurate and interpretable individual mobility prediction, and can be readily applied in downstream applications, including planning on-demand transport services, implementing mobility incentives, and suggesting alternative mobility options.

Check out the paper online and the corresponding code on Github!